Medical-imaging systems produce two-dimensional or three-dimensional images of anatomical structures. Some imaging technologies, such as computer tomography, comprise a sequence of two-dimensional cross-section images, or “slices,” viewed from progressive positions along an axis of a subject's body, that are then combined in sequential order to create a three-dimensional representation of the subject's anatomy.
One potential benefit of digital imaging technology is that it enables downstream computerized imaging systems and image-processing applications to organize, retrieve, analyze, or otherwise process scanned images. But because a 3D scan may extend across an extended region of a subject's body, each 2D cross-section may present the subject's anatomy from a different body position, and thus show different anatomical structures. CT and other imaging technologies thus generally require a way to identify a body position of a 2D or 3D image in order to identify anatomical structures shown in that image and then label the image accordingly.
A medical image may today be labeled by manually comparing the image to “atlas models,” which are known, labeled reference images that are cataloged in an “atlas.” But atlas modeling can be cumbersome and performing such a method without direct human intervention generally requires sophisticated and specialized application software and other imaging resources.
There exists no way to automatically associate a semantic label to an image produced by a medical-imaging system such that the label would allow downstream systems and applications to interpret a subject's body position and anatomical structures shown in the image. There is thus a need for a technical solution to this technical problem, which is necessarily rooted in medical-imaging technology.